We may earn an affiliate commission when you visit our partners.
Janani Ravi

This course explains the continuing relevance of Data Mining today, in the context of applying machine learning techniques to big data. It covers conceptual and practical details of powerful techniques such as Association Rules learning and the industry-standard CRISP-DM methodology for data mining workflows.

Read more

This course explains the continuing relevance of Data Mining today, in the context of applying machine learning techniques to big data. It covers conceptual and practical details of powerful techniques such as Association Rules learning and the industry-standard CRISP-DM methodology for data mining workflows.

Data Mining is an umbrella term used for techniques that find patterns in large datasets. Simply put, data mining is the application of machine learning techniques on big data. The popularity of the term Data Mining peaked some years ago, but in substance, data mining is perhaps more relevant today than it has ever been.

In this course, Data Mining and the Analytics Workflow you will gain the ability to formulate your use-case as a Data Mining problem, and then apply a classic process, the CRISP-DM methodology, to solve it.

First, you will learn how association rules learning works, and why it is considered a classic data mining application, predating the explosion in the popularity of ML. You will see the similarities and contrasts between association rules learning and recommender systems.

Next, you will discover how big data and machine learning both squarely lie within the ambit of data mining, even as more traditional data mining links to statistics and information retrieval continue to exist.

Finally, you will round out your knowledge by learning about an industry-standard process for building data mining applications, know as the CRISP-DM. This technique is about two decades old but has retained its relevance, and closely mirrors the classic machine learning workflow in wide use today.

When you’re finished with this course, you will have the skills and knowledge to design and implement the right data mining solution, one that applies machine learning on big data, for your use-case.

Enroll now

Here's a deal for you

We found an offer that may be relevant to this course.
Save money when you learn. All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

What's inside

Syllabus

Course Overview
Understanding Data Mining
Using Data Mining to Find Patterns
Predictive Data Mining for Categorical Data
Read more
Predictive Data Mining for Continuous Data
Understanding and Implementing the CRISP-DM Methodology

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores data mining, which is standard in ML workflow
Designed for learners interested in ML and data mining
Emphasizes ML techniques in big data context
Introduces CRISP-DM methodology for data mining applications
Instructed by experts in data mining
Requires familiarity with data mining concepts

Save this course

Save Data Mining and the Analytics Workflow to your list so you can find it easily later:
Save

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Mining and the Analytics Workflow with these activities:
Review Statistical Concepts
Strengthen your foundation by reviewing essential statistical concepts used in data mining.
Browse courses on Statistics
Show steps
  • Review basic probability and statistical inference.
  • Focus on concepts relevant to data mining, such as hypothesis testing and regression analysis.
Participate in a Data Mining Study Group
Collaborate with peers to reinforce your understanding and gain different perspectives.
Show steps
  • Find or create a study group with other students taking the course.
  • Meet regularly to discuss course material, solve problems, and share insights.
Read Data Mining: Concepts and Techniques
Gain a comprehensive understanding of data mining concepts and techniques by reading this foundational text.
Show steps
  • Read chapters 1-3 to gain an overview of data mining and its applications.
  • Complete the exercises at the end of each chapter to test your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Data Mining Practice Problems
Sharpen your data mining skills by solving practice problems related to the course topics.
Show steps
  • Identify practice problems related to the specific topics you want to improve.
  • Solve the problems using the techniques learned in the course.
  • Review your solutions and identify areas where you need additional practice.
Follow Tutorials on CRISP-DM Methodology
Enhance your understanding of the CRISP-DM methodology through guided tutorials.
Show steps
  • Find reputable tutorials on the CRISP-DM methodology.
  • Follow the tutorials step-by-step and apply the concepts to a real-world data mining project.
Build a Data Mining Application Using Association Rules
Apply your knowledge by building a practical data mining application that leverages association rules.
Show steps
  • Define the problem statement and data requirements.
  • Collect and preprocess the necessary data.
  • Apply association rules learning techniques to discover patterns in the data.
  • Develop a prototype of the application.
  • Evaluate and refine the application based on user feedback.
Write a Blog Post on Data Mining for Beginners
Solidify your understanding by explaining data mining concepts to a wider audience.
Show steps
  • Identify the key concepts of data mining you want to cover.
  • Write a clear and engaging blog post that explains these concepts in a beginner-friendly manner.
Contribute to Open-Source Data Mining Projects
Gain hands-on experience and contribute to the wider data mining community.
Show steps
  • Find open-source data mining projects that align with your interests.
  • Identify ways to contribute, such as bug fixes, documentation improvements, or new feature development.
  • Submit your contributions to the project and engage with the community.

Career center

Learners who complete Data Mining and the Analytics Workflow will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data scientists, also called data miners, are responsible for extracting knowledge from data by analyzing datasets. This course provides foundational knowledge that can help you become a data scientist by giving you the skills to find patterns in large datasets. It covers conceptual and practical details of powerful techniques such as Association Rules learning and the industry-standard CRISP-DM methodology for data mining workflows, which are essential for data science.
Machine Learning Engineer
Machine learning engineers are responsible for designing, developing, and deploying machine learning models. This course can help you become a machine learning engineer by providing you with a foundation in data mining and machine learning techniques. You will learn how to apply machine learning techniques to big data, and you will also learn about the CRISP-DM methodology for data mining workflows.
Data Analyst
Data analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course can help you become a data analyst by providing you with the skills to find patterns in large datasets and apply machine learning techniques to big data. You will also learn about the CRISP-DM methodology for data mining workflows, which is widely used in the industry.
Business Analyst
Business analysts are responsible for analyzing business data to identify opportunities for improvement. This course can help you become a business analyst by providing you with the skills to find patterns in large datasets and apply machine learning techniques to big data. You will also learn about the CRISP-DM methodology for data mining workflows, which is used in many industries to improve business processes.
Operations Research Analyst
Operations research analysts are responsible for using mathematical and analytical techniques to solve business problems. This course can help you become an operations research analyst by providing you with a foundation in data mining and machine learning techniques. You will learn how to apply machine learning techniques to big data, and you will also learn about the CRISP-DM methodology for data mining workflows.
Statistician
Statisticians are responsible for collecting, analyzing, interpreting, and presenting data. This course can help you become a statistician by providing you with a foundation in data mining and machine learning techniques. You will learn how to apply machine learning techniques to big data, and you will also learn about the CRISP-DM methodology for data mining workflows.
Computer Scientist
Computer scientists are responsible for studying the theory and practice of computation. This course can help you become a computer scientist by providing you with a foundation in data mining and machine learning techniques. You will learn how to apply machine learning techniques to big data, and you will also learn about the CRISP-DM methodology for data mining workflows.
Software Engineer
Software engineers are responsible for designing, developing, and maintaining software applications. This course can help you become a software engineer by providing you with a foundation in data mining and machine learning techniques. You will learn how to apply machine learning techniques to big data, and you will also learn about the CRISP-DM methodology for data mining workflows.
Information Architect
Information architects are responsible for designing and managing the organization of information in websites and other digital products. This course can help you become an information architect by providing you with a foundation in data mining and machine learning techniques. You will learn how to apply machine learning techniques to big data, and you will also learn about the CRISP-DM methodology for data mining workflows.
Database Administrator
Database administrators are responsible for managing and maintaining databases. This course can help you become a database administrator by providing you with a foundation in data mining and machine learning techniques. You will learn how to apply machine learning techniques to big data, and you will also learn about the CRISP-DM methodology for data mining workflows.
Web Developer
Web developers are responsible for designing and developing websites. This course can help you become a web developer by providing you with a foundation in data mining and machine learning techniques. You will learn how to apply machine learning techniques to big data, and you will also learn about the CRISP-DM methodology for data mining workflows.
Data Engineer
Data engineers are responsible for designing, building, and maintaining data pipelines. This course can help you become a data engineer by providing you with a foundation in data mining and machine learning techniques. You will learn how to apply machine learning techniques to big data, and you will also learn about the CRISP-DM methodology for data mining workflows.
UX Designer
UX designers are responsible for designing and evaluating the user experience of websites and other digital products. This course can help you become a UX designer by providing you with a foundation in data mining and machine learning techniques. You will learn how to apply machine learning techniques to big data, and you will also learn about the CRISP-DM methodology for data mining workflows.
Technical Writer
Technical writers are responsible for writing and editing technical documentation. This course can help you become a technical writer by providing you with a foundation in data mining and machine learning techniques. You will learn how to apply machine learning techniques to big data, and you will also learn about the CRISP-DM methodology for data mining workflows.
Project Manager
Project managers are responsible for planning, executing, and controlling projects. This course may be useful for project managers who want to learn more about data mining and machine learning techniques. You will learn how to apply machine learning techniques to big data, and you will also learn about the CRISP-DM methodology for data mining workflows.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Mining and the Analytics Workflow.
Provides a comprehensive overview of data mining concepts and techniques, including data preprocessing, association rule learning, classification, and clustering. It valuable resource for anyone interested in learning more about data mining.
Provides a practical overview of data mining techniques, with a focus on machine learning algorithms. It valuable resource for anyone interested in learning more about data mining and machine learning.
Provides a comprehensive overview of data mining and analytics concepts and techniques, with a focus on real-world applications. It valuable resource for anyone interested in learning more about data mining and analytics.
Provides a comprehensive overview of data mining and knowledge discovery techniques, with a focus on real-world applications. It valuable resource for anyone interested in learning more about data mining and knowledge discovery.
Provides a comprehensive overview of big data analytics, with a focus on strategic planning and enterprise integration. It valuable resource for anyone interested in learning more about big data analytics.
Provides a practical overview of data mining techniques, with a focus on marketing, sales, and customer relationship management applications. It valuable resource for anyone interested in learning more about data mining and how to use it to improve business performance.
Provides a user-friendly overview of data mining concepts and techniques. It valuable resource for anyone interested in learning more about data mining.
Provides a comprehensive overview of data mining techniques, using the Python programming language. It valuable resource for anyone interested in learning more about data mining and how to use it to improve business performance.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Data Mining and the Analytics Workflow.
Evaluating a Data Mining Model
Most relevant
Intro to Analytic Thinking, Data Science, and Data Mining
Most relevant
Association Rules Analysis
Most relevant
More Data Mining with R
Most relevant
Process Mining: Data science in Action
Most relevant
Unlocking the Secrets of Data: Unsupervised Learning with...
Most relevant
A Hands-On Introduction to Process Mining
Most relevant
Unsupervised Learning and Its Applications in Marketing
Most relevant
The Data Science Method
Most relevant
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser